Overview of the Canadian Clinician Investigator Trainees’ research presented at the 2019 CSCI-CITAC Joint Meeting
Bibliographic record
Abstract
The 2019 Annual General Meeting and Young Investigators' Forum of the Canadian Society for Clinical Investigation / Société Canadienne de Recherche Clinique (CSCI/SCRC) and Clinician Investigator Trainee Association of Canada / Association des Cliniciens-Chercheurs en Formation du Canada (CITAC/ACCFC) was held in Banff, Alberta on November 8-10th, 2019. The theme was "Positioning Early Career Investigators for Success: Strategy and Resilience". Lectures and workshops provided knowledge and tools to facilitate the attendees' development as clinician investigators. Dr. Jason Berman (President of CSCI/SCRC), Elina Cook (President of CITAC/ACCFC) and Drs. Doreen Rabi and Zelma Kiss (University of Calgary Organizing Co-Chairs) gave opening presentations. The keynote speakers were Dr. William Foulkes (McGill University) (Distinguished Scientist Award winner) and Dr. Andrés Finzi (Université de Montréal) (Joe Doupe Young Investigator Award winner). Dr. Robert Bortolussi (Dalhousie University) received the Distinguished Service Award for his work as the Editor-in-Chief of Clinical and Investigative Medicine and for being instrumental in the development of the Canadian Child Health Clinician Scientist Program. This meeting was the first to host a panel discussion with Drs. Stephen Robbins and Marcello Tonelli from the Canadian Institutes of Health Research. Workshops on communication, career planning and work-life balance were hosted by André Picard and Drs. Todd Anderson, Karen Tang, William Ghali, May Lynn Quan, Alicia Polachek and Shannon Ruzycki. The AGM showcased 90 presentations from clinician investigator trainees from across Canada. Most of the abstracts are summarized in this review. Eight outstanding abstracts were selected for oral presentation at the President's Forum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".